Abstract
Due to increasing incidents of cyber attacks and heightened concerns for cyber terrorism, implementing effective intrusion detection systems (IDSs) is an essential task for protecting cyber security. Intrusion detection is the process of monitoring and analyzing the events occurring in a computer system in order to detect signs of security problems [1]. Even though the intrusion detection problem has been studied intensively [2], current techniques for intrusion detection still have limitations considering the following three aspects: (1) It is very common to focus on the data mining step, while the other Knowledge Discovery in Databases (KDD) steps are largely ignored [4]. (2) Many intrusion detection systems assume the existence of sharp boundary between normal and anomalous behavior. This assumption, consequently, causes an abrupt separation between normality and anomaly. (3) The construction of many intrusion detection systems is based on some strong assumptions on input data set that make practical applications impractical. Considering all of these limitations, in this paper, we propose a novel anomaly detection framework that has several desirable features.
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© 2006 Springer-Verlag Berlin Heidelberg
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Li, D., Wang, K., Deogun, J.S. (2006). A Fuzzy Anomaly Detection System. In: Chen, H., Wang, FY., Yang, C.C., Zeng, D., Chau, M., Chang, K. (eds) Intelligence and Security Informatics. WISI 2006. Lecture Notes in Computer Science, vol 3917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11734628_25
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DOI: https://doi.org/10.1007/11734628_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33361-6
Online ISBN: 978-3-540-33362-3
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